Sample summary
Input data: breastmilk samples (BM1, BM2, BM3)
- n samples = 1124
- n taxa: genera = 350; variants = 1712
- n taxa (rarefied): genera = 349; variants = 1711
- mean read count = 9.5075^{4}, s.d. = 1.13894^{5}
Statistical methods for mixed-effects models
- Linear mixed-effects models fit with family ID as a random effect
- Effect of country determined by comparing: (i) model with country/age vs (ii) model with age alone
- Pairwise comparisons between countries (M vs I, U vs I, U vs M) corrected by FDR to control family error rate
- OPV/IPV arms from India combined in primary comparison
- Diversity in maternal samples compared by country via ANOVA with post-hoc Tukey test
Taxon abundance profile
Column
Genus-level
India
N: 753
Malawi
N: 243
UK
N: 128
RSV-level
India
Malawi
UK
Alpha diversity
Column
Genus-level
India vs Malawi vs UK - Shannon
India vs Malawi vs UK - richness
India vs Malawi vs UK with cross-sectional p values - Shannon
India vs Malawi vs UK with cross-sectional p values - richness
N by timepoint
IND MLW UK
BM1 274 90 50
BM2 247 79 39
BM3 232 74 39p values, longitudinal mixed-effects models (FDR-adjusted, 4 decimal places)
Shannon Richness
MLW-IND 1e-04 0.0121
UK-IND 0e+00 0.0000
UK-MLW 0e+00 0.0000p values, India (OPV) vs India (IPV)
Shannon Richness
India (OPV)-India (IPV) 0.0205 0.0368p values, cross-sectional models
week of life 1
Shannon Richness
MLW-IND 0.4736 0.0163
UK-IND 0.0000 0.0000
UK-MLW 0.0000 0.0000week of life 7
Shannon Richness
MLW-IND 0.0058 0.2738
UK-IND 0.0000 0.0000
UK-MLW 0.0004 0.0000week of life 11
Shannon Richness
MLW-IND 0.0029 0.9821
UK-IND 0.0000 0.0000
UK-MLW 0.0117 0.0000Longitudinal abundance plots
Infants - India vs UK vs Malawi
N by timepoint
India Malawi UK
BM1 274 90 50
BM2 247 79 39
BM3 232 74 39Infants - India (OPV) vs India (IPV)
Beta diversity
Column
PERMANOVA - unweighted
Inter-individual differences among all infant samples
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
family_ID 460 114.4 0.24869 1.3751 0.48825 0.001 ***
Residuals 663 119.9 0.18085 0.51175
Total 1123 234.3 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Differences by country stratified by age
week R2 p n
1 week of life 1 0.08727212 0.001 414
2 week of life 7 0.07211099 0.001 365
3 week of life 11 0.06956617 0.001 345Summary plot
PERMANOVA - weighted
Inter-individual differences among all infant samples
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
family_ID 460 116.15 0.25250 1.3084 0.47584 0.001 ***
Residuals 663 127.94 0.19298 0.52416
Total 1123 244.10 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Differences by country stratified by age
week R2 p n
1 week of life 1 0.03514650 0.001 414
2 week of life 7 0.04569780 0.001 365
3 week of life 11 0.04065342 0.001 345Summary plot
Differential taxa
Column
Genus-level
Cross-validation accuracy, India vs Malawi vs UK
Pre-vaccination comparisons
Differential abundance plots - significant by Fisher’s test or Aldex2 (FDR p <0.05)
Longitudinal abundance comparisons
Column
Genus-level
Sample profile - country
India Malawi UK
753 243 128 Sample profile - number of infants
India Malawi UK
302 108 51 India vs UK
Number of genera tested: 58
Number of discriminant genera (FDR p < 0.05): 32 (India = 28; UK = 4) Failed to converge: 0
India vs Malawi
Number of genera tested: 70
Number of discriminant genera (FDR p < 0.05): 44 (India = 29; Malawi = 15) Failed to converge: 0
Malawi vs UK
Number of genera tested: 54
Number of discriminant genera (FDR p < 0.05): 32 (Malawi = 26; UK = 6) Failed to converge: 1
Construct tree
Longitudinal abundance plots for major genera
Session info
R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] FSA_0.9.1 ALDEx2_1.26.0
[3] zCompositions_1.4.0 truncnorm_1.0-8
[5] NADA_1.6-1.1 survival_3.2-3
[7] MASS_7.3-55 sjstats_0.18.0
[9] ggExtra_0.9 formattable_0.2.1
[11] NBZIMM_1.0 inlmisc_0.5.2
[13] decontam_1.14.0 ggtree_3.2.1
[15] wesanderson_0.3.6 phangorn_2.8.1
[17] ape_5.6-1 DECIPHER_2.22.0
[19] RSQLite_2.2.9 Biostrings_2.62.0
[21] XVector_0.34.0 cowplot_1.1.1
[23] scales_1.1.1 RVAideMemoire_0.9-81
[25] DescTools_0.99.44 ggsignif_0.6.3
[27] binom_1.1-1 shiny_1.7.1
[29] randomcoloR_1.1.0.1 DESeq2_1.34.0
[31] SummarizedExperiment_1.24.0 Biobase_2.54.0
[33] MatrixGenerics_1.6.0 GenomicRanges_1.46.1
[35] GenomeInfoDb_1.30.0 IRanges_2.28.0
[37] S4Vectors_0.32.3 BiocGenerics_0.40.0
[39] crossval_1.0.4 UpSetR_1.4.0
[41] labdsv_2.0-1 mgcv_1.8-38
[43] nlme_3.1-155 ggpubr_0.4.0
[45] data.table_1.14.2 corrplot_0.92
[47] ZIBR_0.1 vegan_2.5-7
[49] lattice_0.20-45 permute_0.9-7
[51] randomForest_4.6-14 matrixStats_0.61.0
[53] lme4_1.1-27.1 Matrix_1.4-0
[55] reshape2_1.4.4 pheatmap_1.0.12
[57] DT_0.20 plotly_4.10.0
[59] cluster_2.1.2 tidyr_1.1.4
[61] dplyr_1.0.4 magrittr_2.0.1
[63] plyr_1.8.6 kableExtra_1.3.4
[65] gridExtra_2.3 RColorBrewer_1.1-2
[67] knitr_1.37 ggplot2_3.3.5
[69] phyloseq_1.38.0
loaded via a namespace (and not attached):
[1] estimability_1.3 coda_0.19-4 bit64_4.0.5
[4] multcomp_1.4-18 DelayedArray_0.20.0 KEGGREST_1.34.0
[7] RCurl_1.98-1.5 generics_0.1.1 TH.data_1.1-0
[10] terra_1.5-12 proxy_0.4-26 bit_4.0.4
[13] webshot_0.5.2 xml2_1.3.3 httpuv_1.6.5
[16] assertthat_0.2.1 xfun_0.29 jquerylib_0.1.4
[19] evaluate_0.14 promises_1.2.0.1 fansi_1.0.2
[22] igraph_1.2.11 DBI_1.1.2 geneplotter_1.72.0
[25] htmlwidgets_1.5.4 purrr_0.3.4 ellipsis_0.3.2
[28] backports_1.4.1 V8_4.0.0 insight_0.15.0
[31] annotate_1.72.0 vctrs_0.3.8 sjlabelled_1.1.8
[34] abind_1.4-5 cachem_1.0.6 withr_2.4.3
[37] checkmate_2.0.0 rgdal_1.5-28 emmeans_1.7.2
[40] treeio_1.18.1 svglite_2.0.0 lazyeval_0.2.2
[43] crayon_1.4.2 flexdashboard_0.5.2 genefilter_1.76.0
[46] labeling_0.4.2 pkgconfig_2.0.3 rlang_1.0.1
[49] lifecycle_1.0.1 miniUI_0.1.1.1 sandwich_3.0-1
[52] modelr_0.1.8 datawizard_0.2.2 aplot_0.1.2
[55] raster_3.5-11 carData_3.0-5 zoo_1.8-9
[58] Rhdf5lib_1.16.0 boot_1.3-28 png_0.1-7
[61] viridisLite_0.4.0 parameters_0.16.0 rootSolve_1.8.2.3
[64] bitops_1.0-7 rhdf5filters_1.6.0 blob_1.2.2
[67] stringr_1.4.0 rstatix_0.7.0 gridGraphics_0.5-1
[70] memoise_2.0.1 zlibbioc_1.40.0 compiler_4.1.2
[73] cli_3.2.0 ade4_1.7-18 patchwork_1.1.1
[76] tidyselect_1.1.1 stringi_1.7.6 highr_0.9
[79] yaml_2.2.1 locfit_1.5-9.4 grid_4.1.2
[82] sass_0.4.0 fastmatch_1.1-3 tools_4.1.2
[85] lmom_2.8 rstudioapi_0.13 foreach_1.5.1
[88] gld_2.6.4 farver_2.1.0 Rtsne_0.15
[91] RcppZiggurat_0.1.6 digest_0.6.29 quadprog_1.5-8
[94] Rcpp_1.0.8 car_3.0-12 broom_0.7.5
[97] performance_0.8.0 later_1.3.0 httr_1.4.2
[100] AnnotationDbi_1.56.2 effectsize_0.6.0 colorspace_2.0-2
[103] rvest_1.0.2 XML_3.99-0.8 splines_4.1.2
[106] yulab.utils_0.0.4 tidytree_0.3.7 expm_0.999-6
[109] sp_1.4-6 multtest_2.50.0 Exact_3.1
[112] ggplotify_0.1.0 systemfonts_1.0.2 xtable_1.8-4
[115] jsonlite_1.7.3 nloptr_1.2.2.3 Rfast_2.0.4
[118] ggfun_0.0.5 R6_2.5.1 pillar_1.6.4
[121] htmltools_0.5.2 mime_0.12 glue_1.6.0
[124] fastmap_1.1.0 minqa_1.2.4 BiocParallel_1.28.3
[127] class_7.3-20 codetools_0.2-18 mvtnorm_1.1-3
[130] utf8_1.2.2 bslib_0.3.1 tibble_3.1.6
[133] curl_4.3.2 rmarkdown_2.11 biomformat_1.22.0
[136] munsell_0.5.0 e1071_1.7-9 rhdf5_2.38.0
[139] GenomeInfoDbData_1.2.7 iterators_1.0.13 sjmisc_2.8.9
[142] gtable_0.3.0 bayestestR_0.11.5 .